Relational Databases

• PostgreSQL — Open-source, feature-rich, ACID compliance, excellent for complex queries
• MySQL/MariaDB — Lightweight, widely used, good for web applications
• Oracle Database — Enterprise-grade, expensive, highly scalable
• SQL Server — Microsoft’s enterprise database

Key Concepts

• ACID Properties — Atomicity, Consistency, Isolation, Durability
• Normalization — Database design principle to reduce redundancy
• Indexing — Speeds up query performance through data structures (B-trees, hash tables)
• Transactions — Logical units of work maintaining data integrity
• Query Execution Plans — How the database processes queries

NoSQL Databases

• Document Stores — MongoDB, CouchDB (JSON-like documents)
• Key-Value Stores — Redis, DynamoDB (fast caching and sessions)
• Graph Databases — Neo4j (relationships between data)
• Time Series — InfluxDB, Prometheus (metrics, monitoring)
• Wide-Column Stores — Cassandra, HBase (large distributed data)

Query Optimization

Concepts

• Cost-based optimization — Evaluates execution plan efficiency
• Index usage — Choosing appropriate indexes for queries
• Join strategies — Hash joins, nested loop joins, merge joins
• Cardinality estimation — Predicting result set sizes

University Courses

• TUM — Query Optimization
• CMU — Advanced Database Systems

Database Design

• Data Modeling — Conceptual, logical, physical schemas
• Entity-Relationship Model — Designing database structure
• Referential Integrity — Foreign keys and constraints
• Denormalization — Intentional redundancy for performance

Additional Resources

• Andy Pavlo’s lectures on databases
• “Designing Data-Intensive Applications” by Martin Kleppmann
• PostgreSQL and MySQL documentation
• Database courses on platforms like Coursera, edX